Xenium mouse brain - three replicates
Melody Jin
2025-09-08
library(jazzPanda)
library(SpatialExperiment)
library(Seurat)
library(ggplot2)
library(data.table)
library(dplyr)
library(glmnet)
library(caret)
library(ComplexUpset)
library(ggrepel)
library(gridExtra)
library(patchwork)
library(RColorBrewer)
library(limma)
library(here)
library(tidyr)
library(xtable)
source(here("scripts/utils.R"))load the data
rep1=get_xenium_data(path="/stornext/Bioinf/data/lab_phipson/data/xenium_Mm_brain/xenium_prerelease_jun20_mBrain_replicates/mBrain_ff_rep1/",
mtx_name = "cell_feature_matrix_mtx/")
rep2=get_xenium_data(path="/stornext/Bioinf/data/lab_phipson/data/xenium_Mm_brain/xenium_prerelease_jun20_mBrain_replicates/mBrain_ff_rep2/",
mtx_name = "cell_feature_matrix_mtx/")
rep3=get_xenium_data(path="/stornext/Bioinf/data/lab_phipson/data/xenium_Mm_brain/xenium_prerelease_jun20_mBrain_replicates/mBrain_ff_rep3/",
mtx_name = "cell_feature_matrix_mtx/")
rep1$trans_info = rep1$trans_info[rep1$trans_info$qv >=20 & rep1$trans_info$cell_id != -1 & !(rep1$trans_info$cell_id %in% rep1$zero_cells), ]
rep2$trans_info = rep2$trans_info[rep2$trans_info$qv >=20 & rep2$trans_info$cell_id != -1 & !(rep2$trans_info$cell_id %in% rep2$zero_cells), ]
rep3$trans_info = rep3$trans_info[rep3$trans_info$qv >=20 & rep3$trans_info$cell_id != -1 & !(rep3$trans_info$cell_id %in% rep3$zero_cells), ]
data_nm <- "xenium_mbrain"
# load geenrated data
cluster_info = readRDS(here(data_path,paste0(data_nm, "_clusters.Rds")))
anno_df <- data.frame(
cluster = c("c0","c1","c2","c3","c4","c5","c6","c7","c8","c9"),
major_class = c("Glia",
"Neuron (Glut)",
"Neuron (Glut)",
"Neuron (GABA)",
"Non-neuronal",
"Neuron (Glut)",
"Neuron (Glut)",
"Neuron (Glut)",
"Immune",
"Neuron (Glut)"),
subclass = c("OL lineage / Astrocytes",
"IT (intratelencephalic)",
"Projection (CT)",
"Interneuron",
"Vascular / Stromal",
"Hippocampal excitatory",
"Hippocampal excitatory",
"PT (subcortical-projecting)",
"Microglia",
"IT"),
anno_name = c("Astro-OPC/OL mix",
"IT L2/3-4",
"CT-like L6",
"Pvalb interneuron",
"Vascular/ChP fibro-endo",
"CA pyramidal",
"DG granule",
"PT L5",
"Microglia",
"IT Hpcal1"),
Notes = c("Diffuse background, mix of astrocytic (Gfap) and oligodendrocytic (Opalin, Sox10) signals",
"Strong superficial cortical band, Cux1/2, Satb2, Rorb",
"Deep cortical band, Foxp2, Slc17a6, Nell1",
"Sparse across cortex, Gad1/2, Pvalb, Syt2",
"Strands and CP region, Igf2, Pecam1, Col1a1",
"CA1–CA3 band, Slc17a7, Car4, Neurod6",
"Clear DG granule cell layer, Prox1, Calb1",
"Deep cortical stripe, Bcl11b, Penk",
"Scattered puncta, Trem2, Cd68, Siglech",
"Edge-localized cluster, Hpcal1, Cdh4"),
stringsAsFactors = FALSE
)
cluster_info = merge(cluster_info, anno_df, by="cluster")
cluster_names = paste0("c",0:9 )
cluster_info$cluster = factor(cluster_info$cluster,
levels=cluster_names)
# cluster_info = cluster_info[order(cluster_info$cluster), ]
cluster_info$anno_name = factor(cluster_info$anno_name,
levels=anno_df$anno_name)
cluster_info = cluster_info[order(cluster_info$anno_name), ]
jazzPanda_res_lst = readRDS(here(data_path,paste0(data_nm, "_jazzPanda_res_lst.Rds")))
fit.cont = readRDS(here(data_path,paste0(data_nm, "_fit_cont_obj.Rds")))
FM_result= readRDS(here(data_path,paste0(data_nm, "_seu_markers.Rds")))
sv_lst = readRDS(here(data_path,paste0(data_nm, "_sq20_vector_lst.Rds")))
seu = readRDS(here(data_path,paste0(data_nm, "_seu.Rds")))
Idents(seu)=cluster_info$anno_name[match(colnames(seu), cluster_info$cells)]
seu$sample = cluster_info$sample[match(colnames(seu), cluster_info$cells)]Dataset overview
plot_data_sp(cluster_info=cluster_info,file_prefix = data_nm,
my_colors = my_colors, ct_nm = "anno_name")png
2 plot_cluster_props(cluster_info=cluster_info,file_prefix = data_nm,
my_colors = my_colors, ct_nm = "anno_name")png
2 plot_umap_seu(cluster_info=cluster_info,seu=seu, file_prefix = data_nm,
my_colors = my_colors, ct_nm = "anno_name",fig_w = 3000)png
2 Spatial pattern of negative controls
nc_sp1 <- rep1$trans_info[rep1$trans_info$feature_name %in% c(rep1$probe, rep1$codeword),
c("x", "y", "feature_name")]
nc_sp2 <- rep2$trans_info[rep2$trans_info$feature_name %in% c(rep2$probe, rep2$codeword),
c("x", "y", "feature_name")]
nc_sp3 <- rep3$trans_info[rep3$trans_info$feature_name %in% c(rep3$probe, rep3$codeword),
c("x", "y", "feature_name")]
nc_coords = as.data.frame(rbind(cbind(nc_sp1, sample= "replicate1"),
cbind(nc_sp2, sample= "replicate2"),
cbind(nc_sp2, sample= "replicate3"))
)
nc_coords$category = "negative control probe"
nc_coords[nc_coords$feature_name %in% c(rep1$codeword,rep2$codeword,rep3$codeword), "category"] = "negative control codeword"
cat("#negative controls", length(unique((nc_coords$feature_name))), "\n")#negative controls 81 nc_coords$category=factor(nc_coords$category,
levels =c("negative control probe",
"negative control codeword"))
jpeg(file.path(overview_PA, paste0(data_nm, "_nc_xy.jpg")),
width=600*2, height=800*3, res=200)
ggplot(nc_coords, aes(x=x, y=y))+
geom_point(size=0.01, color="black")+
theme_bw()+
facet_wrap(category~sample)+
scale_y_reverse()+
defined_theme+
theme(strip.text = element_text(size=8),
strip.background = element_rect(fill =NA,colour = "black"))
dev.off()png
2 jazzPanda-glm
jazzPanda_res = get_top_mg(jazzPanda_res_lst, coef_cutoff=0.1)
jazzPanda_summary = as.data.frame(table(jazzPanda_res$top_cluster))
colnames(jazzPanda_summary) = c("cluster","jazzPanda_glm")# Combine into a new dataframe for LaTeX output
output_data <- do.call(rbind, lapply(cluster_names, function(cl) {
subset <- jazzPanda_res[jazzPanda_res$top_cluster == cl, ]
sorted_subset <- subset[order(-subset$glm_coef), ]
gene_list <- paste(sorted_subset$gene, "(", round(sorted_subset$glm_coef, 2), ")", sep="", collapse=", ")
return(data.frame(Cluster = cl, Genes = gene_list))
}))
latex_table <- xtable(output_data, caption="Detected marker genes for each cluster by jazzPanda, in decreasing value of glm coefficients")
print.xtable(latex_table, include.rownames = FALSE, hline.after = c(-1, 0, nrow(output_data)), comment = FALSE)top3 marker genes by jazzPanda-glm
xr <- c(0, 3000)
yr <- c(0, 4135)
plot_lst=list()
for (cl in paste("c", 0:9, sep="")){
ct_nm = anno_df[anno_df$cluster==cl, "anno_name"]
inters=jazzPanda_res[jazzPanda_res$top_cluster==cl,"gene"]
rounded_val=signif(as.numeric(jazzPanda_res[inters,"glm_coef"]),
digits = 3)
inters_df = as.data.frame(cbind(gene=inters, value=rounded_val))
inters_df$value = as.numeric(inters_df$value)
inters_df=inters_df[order(inters_df$value, decreasing = TRUE),]
inters_df$text= paste(inters_df$gene,inters_df$value,sep=": ")
if (length(inters > 0)){
inters_df = inters_df[1:min(3, nrow(inters_df)),]
inters = inters_df$gene
all_trans = as.data.frame(matrix(0, ncol=6))
colnames(all_trans) = c("x","y", "feature_name","value","text_label","sample")
for (rp in c("rep1","rep2","rep3")){
rp_lst = get(rp)
iters_sp1= rp_lst$trans_info$feature_name %in% inters
vis_r1 =rp_lst$trans_info[iters_sp1,
c("x","y","feature_name")]
vis_r1$value = inters_df[match(vis_r1$feature_name,inters_df$gene),
"value"]
vis_r1=vis_r1[order(vis_r1$value,decreasing = TRUE),]
vis_r1$text_label= paste(vis_r1$feature_name,
vis_r1$value,sep=": ")
vis_r1$text_label=factor(vis_r1$text_label, levels=inters_df$text)
vis_r1$sample=paste("replicate", substr(rp,start = 4, stop=4),sep="")
all_trans = rbind(all_trans,vis_r1)
}
all_trans = all_trans[2:nrow(all_trans),]
all_trans$text_label = factor(all_trans$text_label,
levels = inters_df$text)
all_trans = all_trans[order(all_trans$text_label),]
genes_plt<- ggplot(data = all_trans,
aes(x = x, y = y))+
#geom_point(size=0.0001)+
geom_hex(bins = auto_hex_bin(mean(table(all_trans$sample,
all_trans$feature_name))))+
facet_grid(sample~text_label)+
scale_x_reverse() +
scale_fill_gradient(low="white", high="maroon4") +
#scale_color_manual(values = c("maroon4","maroon4","maroon4"))+
guides(fill = guide_colorbar(barheight = unit(0.2, "npc"),
barwidth = unit(0.03, "npc")))+
defined_theme+ theme(legend.position = "right",
strip.background = element_rect(fill =NA,colour = NA),
strip.text = element_text(size = rel(1.2)),
strip.text.y.right = element_blank(),
legend.key.width = unit(2, "cm"),
legend.title = element_text(size = 10),
legend.text = element_text(size = 10),
legend.key.height = unit(2, "cm"))
cluster_data = cluster_info[cluster_info$cluster==cl, ]
cluster_data$label = paste(cluster_data$sample, cluster_data$anno_name, sep="-")
cluster_data$label =factor(cluster_data$label,
levels=paste(paste("replicate",1:3,sep=""),
ct_nm,sep="-"))
p_cl<- ggplot(data =cluster_data,
aes(x = x, y = y, color=sample))+
#geom_hex(bins = 100)+
geom_point(size=0.01)+
facet_wrap(~label, nrow=3,strip.position = "left")+
scale_x_reverse() +
scale_color_manual(values = c("black","black","black"))+
#scale_fill_gradient(low="white", high="maroon4") +
#guides(fill = guide_colorbar(barwidth = unit(15, "cm"),height= unit(30, "cm")))+
defined_theme + theme(legend.position = "none",
strip.background = element_blank(), legend.key.width = unit(15, "cm"),
strip.text = element_text(size = rel(1.2)))
lyt <-p_cl | genes_plt
layout_design <- lyt + patchwork::plot_layout(heights = c(1,1),widths = c(1, 3))
plot_lst[[cl]] = print(layout_design)
cat("cluster:", cl,"\n")
jpeg(file.path(mg_PA,paste0(data_nm, "_glm_", cl,"_top3.jpg")),
width=600*4, height=800*3, res=200)
print(layout_design)
dev.off()
}
}cluster: c0 cluster: c1 cluster: c2 cluster: c3 cluster: c4 cluster: c5 cluster: c6 cluster: c7 cluster: c8 cluster: c9 
## cluster vector versus marker gene vector
plot_lst=list()
nbins = 400
for (cl in cluster_names){
ct_nm = anno_df[anno_df$cluster==cl, "anno_name"]
inters_df=jazzPanda_res[jazzPanda_res$top_cluster==cl,]
inters_df=inters_df[order(inters_df$glm_coef, decreasing = TRUE),]
inters_df = inters_df[1:min(3, nrow(inters_df)),]
mk_genes = inters_df$gene
dff = as.data.frame(cbind(sv_lst$cluster_mt[,cl],
sv_lst$gene_mt[,mk_genes]))
colnames(dff) = c("cluster", mk_genes)
dff$sample= "replicate1"
dff[400:800,"sample"] = "replicate2"
dff[800:1200,"sample"] = "replicate3"
dff$vector_id = c(1:400, 1:400, 1:400)
long_df <- dff %>%
pivot_longer(cols = -c(cluster, sample, vector_id), names_to = "gene",
values_to = "vector_count")
long_df$gene = factor(long_df$gene, levels=mk_genes)
long_df$sample = factor(long_df$sample ,
levels=c("replicate1","replicate2", "replicate3"))
p<-ggplot(long_df, aes(x = cluster, y = vector_count, color=gene )) +
geom_point(size=0.01) +
facet_grid(sample~gene, scales="free_y") +
labs(x = paste(ct_nm, " cluster vector ", sep=""),
y = "Top3 marker gene vector") +
theme_minimal()+
scale_y_continuous(expand = c(0.01,0.01))+
scale_x_continuous(expand = c(0.01,0.01))+
theme(panel.grid = element_blank(),legend.position = "none",
strip.text = element_text(size = 12),
axis.line=element_blank(),
axis.text=element_text(size = 11),
axis.ticks=element_line(color="black"),
axis.title=element_text(size = 13),
panel.border =element_rect(colour = "black",
fill=NA, linewidth=0.5)
)
plot_lst[[cl]] = p
}
n_per_page <- 6
rem <- length(plot_lst) %% n_per_page
if (rem != 0) {
n_pad <- n_per_page - rem
plot_lst <- c(
plot_lst,
rep(list(patchwork::plot_spacer()), n_pad)
)
}
# Split into 2×2 pages
plot_chunks <- split(plot_lst, ceiling(seq_along(plot_lst) / n_per_page))
pdf(file.path(mg_PA, paste0(data_nm, "_glm_mg_vvplot.pdf")),
height = 12, width = 12)
for (chunk in plot_chunks) {
# arrange 2 columns, auto rows
p <- wrap_plots(chunk, ncol = 2) +
plot_layout(heights = c(1,1,1), widths = c(1,1))
print(p)
}
dev.off()png
2 Compare marker genes by different methods
FindMarker and limma result
table(FM_result$cluster)
FM_result$cluster <- factor(FM_result$cluster, levels = paste0("c", 0:9))
top_mg <- FM_result %>%
group_by(cluster) %>%
arrange(desc(avg_log2FC), .by_group = TRUE) %>%
slice_max(order_by = avg_log2FC, n = 10) %>%
select(cluster, gene, avg_log2FC)
uni_clusters <-paste0("c", 0:9)
# Combine into a new dataframe for LaTeX output
output_data <- do.call(rbind, lapply(uni_clusters, function(cl) {
subset <- top_mg[top_mg$cluster == cl, ]
sorted_subset <- subset[order(-subset$avg_log2FC), ]
gene_list <- paste(sorted_subset$gene, "(", round(sorted_subset$avg_log2FC, 2), ")", sep="", collapse=", ")
return(data.frame(Cluster = cl, Genes = gene_list))
}))
latex_table <- xtable(output_data, caption="Detected marker genes for each cluster by FindMarkers")
print.xtable(latex_table, include.rownames = FALSE, hline.after = c(-1, 0, nrow(output_data)), comment = FALSE)limma_dt <- decideTests(fit.cont)
summary(limma_dt) c0 c1 c2 c3 c4 c5 c6 c7 c8 c9
Down 201 120 155 132 189 171 212 207 176 158
NotSig 4 20 10 14 11 15 3 5 38 35
Up 79 144 119 138 84 98 69 72 70 91Cumulative rank plot
plot_lst=list()
cor_M = cor(sv_lst$gene_mt,
sv_lst$cluster_mt[, paste0("c", 0:9)],
method = "spearman")
cor_M[cor_M <= 0.5] = 0
cor_M[cor_M > 0.5]=1
Idents(seu)=cluster_info$cluster[match(colnames(seu), cluster_info$cells)]
for (cl in paste("c", 0:9, sep="")){
ct_nm = anno_df[anno_df$cluster==cl, "anno_name"]
fm_cl=FindMarkers(seu, ident.1 = cl, only.pos = TRUE,
logfc.threshold = 0.25)
fm_cl = fm_cl[order(fm_cl$avg_log2FC, decreasing = TRUE),]
to_plot =row.names(fm_cl)
FM_pt = data.frame("name"=to_plot,"rk"= 1:length(to_plot),
"y"= cumsum(cor_M[to_plot, cl]),
"type"="FindMarkers")
limma_cl<-topTable(fit.cont,coef=cl,p.value = 0.05, n=Inf, sort.by = "p")
limma_cl = limma_cl[limma_cl$logFC>0, ]
to_plot_lm = row.names(limma_cl)
lasso_sig = jazzPanda_res[jazzPanda_res$top_cluster==cl,]
lasso_sig = lasso_sig[order(lasso_sig$glm_coef, decreasing = TRUE),]
limma_pt = data.frame("name"=to_plot_lm,"rk"= 1:length(to_plot_lm),
"y"= cumsum(cor_M[to_plot_lm, cl]),
"type"="limma")
lasso_pt = data.frame("name"=lasso_sig$gene,"rk"= 1:nrow(lasso_sig),
"y"= cumsum(cor_M[lasso_sig$gene, cl]),
"type"="jazzPanda-glm")
data_lst = rbind(limma_pt, lasso_pt,FM_pt)
data_lst$type <- factor(data_lst$type,
levels = c("limma", "FindMarkers", "jazzPanda-glm"),
labels = c("limma", "Wilcoxon Rank Sum Test", "jazzPanda-glm")
)
data_lst$rk = as.numeric(data_lst$rk)
p <-ggplot(data_lst, aes(x = rk, y = y, color = type)) +
geom_step(size = 0.8) + # type = "s"
scale_color_manual(values = c("limma" = "black",
"Wilcoxon Rank Sum Test" = "blue",
"jazzPanda-glm" = "red")) +
labs(title = ct_nm, x = "Rank of marker genes",
y = "Cumulative count of genes with correlation >0.5",
color = NULL) +
theme_classic(base_size = 12) +
theme(plot.title = element_text(hjust = 0.5),
axis.line = element_blank(),
panel.border = element_rect(color = "black",
fill = NA, linewidth = 1),
legend.position = "inside",
legend.position.inside = c(0.98, 0.02),
legend.justification = c("right", "bottom"),
legend.background = element_rect(color = "black",
fill = "white", linewidth = 0.5),
legend.box.background = element_rect(color = "black",
linewidth = 0.5)
)
plot_lst[[cl]] <- p
}
combined_plot <- wrap_plots(plot_lst, ncol = 3)
pdf(file.path(comp_PA, paste0(data_nm, "_mg_compare_rank.pdf")),
height = 20, width = 15)
combined_plot
dev.off()png
2 upset plot
plot_lst=list()
for (cl in paste("c", 0:9, sep="")){
ct_nm = anno_df[anno_df$cluster==cl, "anno_name"]
findM_sig <- FM_result[FM_result$cluster==cl,"gene"]
limma_sig <- row.names(limma_dt[limma_dt[,cl]==1,])
lasso_cl <- jazzPanda_res[jazzPanda_res$top_cluster==cl, "gene"]
df_mt <- as.data.frame(matrix(FALSE,nrow=nrow(jazzPanda_res),ncol=3))
row.names(df_mt) <- jazzPanda_res$gene
colnames(df_mt) <- c("jazzPanda-glm",
"Wilcoxon Rank Sum Test","limma")
df_mt[findM_sig,"Wilcoxon Rank Sum Test"] <- TRUE
df_mt[limma_sig,"limma"] <- TRUE
df_mt[lasso_cl,"jazzPanda-glm"] <- TRUE
df_mt$gene_name <- row.names(df_mt)
p<-upset(df_mt,
intersect=c("limma","Wilcoxon Rank Sum Test","jazzPanda-glm"),
wrap=TRUE, keep_empty_groups= TRUE, name="",
themes=theme_grey(),
stripes=upset_stripes(geom=geom_segment(size=5),colors=c( 'grey95', 'grey95','grey95')),
sort_intersections_by ="cardinality", sort_sets= FALSE,min_degree=1,
set_sizes=FALSE,
sort_intersections= "descending", warn_when_converting=FALSE,
warn_when_dropping_groups=TRUE,encode_sets=TRUE,
matrix=(intersection_matrix()+
theme(axis.text.x=element_blank(),panel.background = element_rect(fill="NA"),
axis.ticks = element_blank(),
axis.title = element_blank())),
base_annotations=list('Intersection size'=(intersection_size(bar_number_threshold=1,color='grey9',fill='grey80')+
scale_y_continuous(expand = expansion(mult = c(0, 0.15)))+
theme(axis.text.x = element_blank(),axis.title.x = element_blank(),
panel.background = element_rect(fill="NA"),
panel.grid = element_line(color="grey90"),
axis.ticks.x = element_blank()))),
width_ratio=0.5, height_ratio=1/4)+
ggtitle(ct_nm)
plot_lst[[cl]] <- p
}
combined_plot <- wrap_plots(plot_lst, ncol = 4)
pdf(file.path(comp_PA, paste0(data_nm, "_mg_compare_upset.pdf")),
height =4* (ceiling(length(cluster_names)/4)), width = 16)
combined_plot
dev.off()png
2 sessionInfo()R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Red Hat Enterprise Linux 9.3 (Plow)
Matrix products: default
BLAS: /stornext/System/data/software/rhel/9/base/tools/R/4.5.0/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.5.0/lib64/R/lib/libRlapack.so; LAPACK version 3.12.1
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Melbourne
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] xtable_1.8-4 tidyr_1.3.1
[3] here_1.0.1 limma_3.64.1
[5] RColorBrewer_1.1-3 patchwork_1.3.1
[7] gridExtra_2.3 ggrepel_0.9.6
[9] ComplexUpset_1.3.3 caret_7.0-1
[11] lattice_0.22-6 glmnet_4.1-10
[13] Matrix_1.7-3 dplyr_1.1.4
[15] data.table_1.17.8 ggplot2_3.5.2
[17] Seurat_5.3.0 SeuratObject_5.1.0
[19] sp_2.2-0 SpatialExperiment_1.18.1
[21] SingleCellExperiment_1.30.1 SummarizedExperiment_1.38.1
[23] Biobase_2.68.0 GenomicRanges_1.60.0
[25] GenomeInfoDb_1.44.2 IRanges_2.42.0
[27] S4Vectors_0.46.0 BiocGenerics_0.54.0
[29] generics_0.1.4 MatrixGenerics_1.20.0
[31] matrixStats_1.5.0 jazzPanda_0.2.4
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.5.0 later_1.4.2
[4] R.oo_1.27.1 tibble_3.3.0 polyclip_1.10-7
[7] hardhat_1.4.2 pROC_1.19.0.1 rpart_4.1.24
[10] fastDummies_1.7.5 lifecycle_1.0.4 rprojroot_2.1.0
[13] globals_0.18.0 MASS_7.3-65 magrittr_2.0.3
[16] plotly_4.11.0 sass_0.4.10 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.16
[22] sctransform_0.4.2 spam_2.11-1 spatstat.sparse_3.1-0
[25] reticulate_1.43.0 cowplot_1.2.0 pbapply_1.7-4
[28] lubridate_1.9.4 abind_1.4-8 Rtsne_0.17
[31] presto_1.0.0 purrr_1.1.0 R.utils_2.13.0
[34] BumpyMatrix_1.16.0 nnet_7.3-20 ipred_0.9-15
[37] lava_1.8.1 GenomeInfoDbData_1.2.14 irlba_2.3.5.1
[40] listenv_0.9.1 spatstat.utils_3.1-5 goftest_1.2-3
[43] RSpectra_0.16-2 spatstat.random_3.4-1 fitdistrplus_1.2-4
[46] parallelly_1.45.1 codetools_0.2-20 DelayedArray_0.34.1
[49] tidyselect_1.2.1 shape_1.4.6.1 UCSC.utils_1.4.0
[52] farver_2.1.2 spatstat.explore_3.5-2 jsonlite_2.0.0
[55] progressr_0.15.1 ggridges_0.5.6 survival_3.8-3
[58] iterators_1.0.14 foreach_1.5.2 tools_4.5.0
[61] ica_1.0-3 Rcpp_1.1.0 glue_1.8.0
[64] prodlim_2025.04.28 SparseArray_1.8.1 xfun_0.52
[67] withr_3.0.2 fastmap_1.2.0 digest_0.6.37
[70] timechange_0.3.0 R6_2.6.1 mime_0.13
[73] colorspace_2.1-1 scattermore_1.2 tensor_1.5.1
[76] spatstat.data_3.1-8 R.methodsS3_1.8.2 hexbin_1.28.5
[79] recipes_1.3.1 class_7.3-23 httr_1.4.7
[82] htmlwidgets_1.6.4 S4Arrays_1.8.1 uwot_0.2.3
[85] ModelMetrics_1.2.2.2 pkgconfig_2.0.3 gtable_0.3.6
[88] timeDate_4041.110 lmtest_0.9-40 XVector_0.48.0
[91] htmltools_0.5.8.1 dotCall64_1.2 scales_1.4.0
[94] png_0.1-8 gower_1.0.2 spatstat.univar_3.1-4
[97] knitr_1.50 reshape2_1.4.4 rjson_0.2.23
[100] nlme_3.1-168 cachem_1.1.0 zoo_1.8-14
[103] stringr_1.5.1 KernSmooth_2.23-26 parallel_4.5.0
[106] miniUI_0.1.2 pillar_1.11.0 grid_4.5.0
[109] vctrs_0.6.5 RANN_2.6.2 promises_1.3.3
[112] cluster_2.1.8.1 evaluate_1.0.4 magick_2.8.7
[115] cli_3.6.5 compiler_4.5.0 rlang_1.1.6
[118] crayon_1.5.3 future.apply_1.20.0 labeling_0.4.3
[121] plyr_1.8.9 stringi_1.8.7 viridisLite_0.4.2
[124] deldir_2.0-4 BiocParallel_1.42.1 lazyeval_0.2.2
[127] spatstat.geom_3.5-0 RcppHNSW_0.6.0 future_1.67.0
[130] statmod_1.5.0 shiny_1.11.1 ROCR_1.0-11
[133] igraph_2.1.4 bslib_0.9.0